1. Field of the Invention
The present invention relates to data processing of financial securities using automated electrical financial analysis, and more particularly to a method of administering an asset-backed security (ABS) such as a mortgage-backed security (MBS) or a collateralized mortgage obligation (CMO).
2. Description of Related Art
A mortgage-backed security (MBS) is a type of an asset-backed security (ABS), which is derived from a pool of mortgages. Specifically, a collection of mortgages are gathered to form a pool, usually according to common, pre-selected characteristics such as interest rate and amortization as well as other characteristics. The pool of mortgages generate cashflows through the payments of principal and interest by the mortgagors. A specified portion of these cashflows is passed to the holder of the mortgage-backed security.
One type of a mortgage-backed security is the mortgage passthrough security. Typically, a holder of such a security receives all of the principal cashflows and part of the interest cashflows. The interest cashflows received by the MBS holder is based upon a fixed interest rate applied to the outstanding principal. For instance, a mortgage-backed security based on a pool of conforming residential mortgages, typically pays a coupon that is 0.5% below the weighted average coupon of the mortgages. The difference is retained by the issuer for servicing costs, default risk, and profit.
Additionally, mortgage-backed securities can be created by decomposing the cashflows generated by a pool of mortgages into tranches, called collateralized mortgage obligations (CMOs). The principal payments typically flow through to the tranches on a pro-rata or sequential basis. Each tranche has its own fixed or floating interest rate. An important example is a principal only (PO) strip, which passes through only principal payments and no interest payments.
The cashflows generated from these mortgages are susceptible to change due to prepayments made by the mortgagors. These changes affect the value of the MBS and the possibility of their occurrence is called prepayment risk. Prepayment modeling is the dominant consideration of MBS valuation. Projections of future prepayments are typically derived from historical data.
Prepayment risk has different causes. For instance, the mortgagor may sell the mortgaged house to move to a different location. If the mortgagors default on their obligations, the remaining principal is paid by the issuer of the mortgage-backed securities to the holders, resulting in prepayment. Additionally, certain mortgagors will make extra principal payments on a monthly basis to reduce future payments.
Further still, some mortgagors will refinance mortgages for purely economic reasons, for instance, when refinancing rates are lower than the rate on the outstanding mortgage. Unlike other types of prepayments, refinancing prepayments are primarily driven by interest rates, which behave unpredictably.
Hence, it can be seen that prepayment is a dominant factor in the valuation of mortgage-backed securities. Valuation is essential for portfolio management and the structuring and trading of the mortgage-backed securities. The prepayments depend on interest rates (which drive refinancings), demographics (which affect home sales and foreclosure), and real estate values (which affect refinancings for converting equity into cash), home sales, and foreclosures.
To assist in accounting for prepayments in the valuation of mortgage-backed securities, historical prepayment data is available and often used. Major industry players even use loan-level, obligor-specific data to refine prepayment models. The expected prepayment behavior is estimated statistically from historical data. There are complications, however, such as “burnout” where there is a slow-down of prepayments following an active period of refinancings. The burnout is typically modeled by adjusting the input parameters of a model according to historical burnout patterns.
An example of the effect of prepayment on the present value of a mortgage-backed security is graphically illustrated in FIG. 1. Because the prepayments result in payment of principal, the present value of the MBS in the presence of prepayments 10 is initially higher than the present value 11 if there were no possibility of prepayments. However, over time the present value will drop below the level that would otherwise be expected if prepayments were not a factor.
Since the introduction of mortgage-backed securities over two decades ago, the market has seen an enormous volume of research pertaining to the prepayment behavior of residential mortgages, with the aim of improving the pricing of mortgage-backed securities and associated derivative structures.
Most of this research consists of statistical analysis of historical pool-level prepayment data. The recent trend has been to refine this approach by incorporating loan-level information. But in spite of the considerable effort, the predictive power of prepayment models has been rather disappointing.
Although the right to prepay is generally recognized as a formal option imposed by applicable law, option-based modeling is virtually absent in MBS analysis. Prepayment formulas driving MBS valuation engines depend on the yield curve, but not on the volatility that gives rise to the curve.
An exception was an effort at Merrill Lynch circa 1987 and 1988, described in Andrew Davidson, The Refinancing Threshold Pricing Model: An Economic Approach to Valuing MBS, Journal of Real Estate Finance and Economics, June 1988. This model attributed variations in prepayments to variations in transaction costs, grouped mortgagors by transaction cost, and determined optimal refinancings based on transaction costs. While the approach was a step in the right direction, it did not model mortgage prepayments sufficiently rigorously. In particular, the model did not distinguish refinancings from other types of prepayments, nor did it consider an optionless mortgage yield curve, distinct and separate from the MBS curve. In addition, since transaction costs were assumed to be positive, this approach does not allow for the existence of leapers (described below). Evidently the approach gained limited acceptance and was eventually abandoned.
In general, the values of mortgage-backed securities have typically been determined by Monte Carlo simulation. Monte Carlo simulation is used extensively in finance for such tasks as pricing derivatives or estimating the ‘value at risk’ of portfolios.
The valuation of the mortgage-backed security by Monte Carlo simulation involves generating hundreds, if not thousands, of interest rate scenarios, calculating the cashflows generated by the mortgage pool under each scenario, and thereafter calculating the cashflow passed through to the mortgage-backed security for each scenario. The cashflows are then valued using the scenario-dependent interest rates. The mortgage-backed security value is the average of the scenario-dependent values.
Monte Carlo simulation is computationally intensive. It is particularly difficult to obtain accurate estimates of risk using Monte Carlo simulation. The conventional mechanism for valuing mortgage-backed securities is less than ideal for at least this reason.